# Systematic Literature Review on Application of Learning-based Approaches in Continuous Integration

arXiv: https://arxiv.org/abs/2406.19765 · HTML: https://mlsanity.com/paper/2406.19765/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 102 references that are in the map.
- First posted on arXiv 28 Jun 2024.

## Builds on (most cited first, 16 of 16)

- [Reinforcement learning](https://mlsanity.com/paper/2405.10369/) (2024)
- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [Learning with Multiclass AUC: Theory and Algorithms](https://mlsanity.com/paper/2107.13171/) (2021)
- [Continuous Integration, Delivery and Deployment: A Systematic Review on Approaches, Tools, Challenges and Practices](https://mlsanity.com/paper/1703.07019/) (2017)
- [Detection of data drift and outliers affecting machine learning model performance over time](https://mlsanity.com/paper/2012.09258/) (2020)
- [Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration](https://mlsanity.com/paper/1811.04122/) (2018)
- [Reinforcement Learning for Test Case Prioritization](https://mlsanity.com/paper/2011.01834/) (2020)
- [Test Case Selection and Prioritization Using Machine Learning: A Systematic Literature Review](https://mlsanity.com/paper/2106.13891/) (2021)
- [DeepOrder: Deep Learning for Test Case Prioritization in Continuous Integration Testing](https://mlsanity.com/paper/2110.07443/) (2021)
- [Scalable and Accurate Test Case Prioritization in Continuous Integration Contexts](https://mlsanity.com/paper/2109.13168/) (2021)
- [Data stream mining for predicting software build outcomes using source code metrics](https://mlsanity.com/paper/1604.05503/) (2016)
- [Test Prioritization in Continuous Integration Environments](https://mlsanity.com/paper/1809.00143/) (2018)
- [Machine Learning for Software Engineering: A Systematic Mapping](https://mlsanity.com/paper/2005.13299/) (2020)
- [Rethinking Default Values: a Low Cost and Efficient Strategy to Define Hyperparameters](https://mlsanity.com/paper/2008.00025/) (2020)
- [SoK: Machine Learning for Continuous Integration](https://mlsanity.com/paper/2304.02829/) (2023)
- [Towards Overfitting Avoidance: Tuning-free Tensor-aided Multi-user Channel Estimation for 3D Massive MIMO Communications](https://mlsanity.com/paper/2101.09672/) (2021)

## Built on by (most cited first, 6 of 6)

- [LLMSecConfig: An LLM-Based Approach for Fixing Software Container Misconfigurations](https://mlsanity.com/paper/2502.02009/) (2025)
- [Mitigating Data Imbalance for Software Vulnerability Assessment: Does Data Augmentation Help?](https://mlsanity.com/paper/2407.10722/) (2024)
- [Automated Code-centric Software Vulnerability Assessment: How Far Are We? An Empirical Study in C/C++](https://mlsanity.com/paper/2407.17053/) (2024)
- [Automatic Data Labeling for Software Vulnerability Prediction Models: How Far Are We?](https://mlsanity.com/paper/2407.17803/) (2024)
- [Targeted Test Selection Approach in Continuous Integration](https://mlsanity.com/paper/2509.10279/) (2025)
- [Understanding the Impact of AI Code Assistants on Security API Usage: An Empirical Study](https://mlsanity.com/paper/2607.11348/) (2026)

## Abstract (arXiv)

Context: Machine learning (ML) and deep learning (DL) analyze raw data to extract valuable insights in specific phases. The rise of continuous practices in software projects emphasizes automating Continuous Integration (CI) with these learning-based methods, while the growing adoption of such approaches underscores the need for systematizing knowledge. Objective: Our objective is to comprehensively review and analyze existing literature concerning learning-based methods within the CI domain. We endeavour to identify and analyse various techniques documented in the literature, emphasizing the fundamental attributes of training phases within learning-based solutions in the context of CI. Method: We conducted a Systematic Literature Review (SLR) involving 52 primary studies. Through statistical and thematic analyses, we explored the correlations between CI tasks and the training phases of learning-based methodologies across the selected studies, encompassing a spectrum from data engineering techniques to evaluation metrics. Results: This paper presents an analysis of the automation of CI tasks utilizing learning-based methods. We identify and analyze nine types of data sources, four steps in data preparation, four feature types, nine subsets of data features, five approaches for hyperparameter selection and tuning, and fifteen evaluation metrics. Furthermore, we discuss the latest techniques employed, existing gaps in CI task automation, and the characteristics of the utilized learning-based techniques. Conclusion: This study provides a comprehensive overview of learning-based methods in CI, offering valuable insights for researchers and practitioners developing CI task automation. It also highlights the need for further research to advance these methods in CI.